11 research outputs found
ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LCâMS/MS Experiments
Detection of differentially abundant
proteins in label-free quantitative shotgun liquid chromatographyâtandem
mass spectrometry (LCâMS/MS) experiments requires a series
of computational steps that identify and quantify LCâMS features.
It also requires statistical analyses that distinguish systematic
changes in abundance between conditions from artifacts of biological
and technical variation. The 2015 study of the Proteome Informatics
Research Group (iPRG) of the Association of Biomolecular Resource
Facilities (ABRF) aimed to evaluate the effects of the statistical
analysis on the accuracy of the results. The study used LCâtandem
mass spectra acquired from a controlled mixture, and made the data
available to anonymous volunteer participants. The participants used
methods of their choice to detect differentially abundant proteins,
estimate the associated fold changes, and characterize the uncertainty
of the results. The study found that multiple strategies (including
the use of spectral counts versus peak intensities, and various software
tools) could lead to accurate results, and that the performance was
primarily determined by the analystsâ expertise. This manuscript
summarizes the outcome of the study, and provides representative examples
of good computational and statistical practice. The data set generated
as part of this study is publicly available
ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LCâMS/MS Experiments
Detection of differentially abundant
proteins in label-free quantitative shotgun liquid chromatographyâtandem
mass spectrometry (LCâMS/MS) experiments requires a series
of computational steps that identify and quantify LCâMS features.
It also requires statistical analyses that distinguish systematic
changes in abundance between conditions from artifacts of biological
and technical variation. The 2015 study of the Proteome Informatics
Research Group (iPRG) of the Association of Biomolecular Resource
Facilities (ABRF) aimed to evaluate the effects of the statistical
analysis on the accuracy of the results. The study used LCâtandem
mass spectra acquired from a controlled mixture, and made the data
available to anonymous volunteer participants. The participants used
methods of their choice to detect differentially abundant proteins,
estimate the associated fold changes, and characterize the uncertainty
of the results. The study found that multiple strategies (including
the use of spectral counts versus peak intensities, and various software
tools) could lead to accurate results, and that the performance was
primarily determined by the analystsâ expertise. This manuscript
summarizes the outcome of the study, and provides representative examples
of good computational and statistical practice. The data set generated
as part of this study is publicly available
ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LCâMS/MS Experiments
Detection of differentially abundant
proteins in label-free quantitative shotgun liquid chromatographyâtandem
mass spectrometry (LCâMS/MS) experiments requires a series
of computational steps that identify and quantify LCâMS features.
It also requires statistical analyses that distinguish systematic
changes in abundance between conditions from artifacts of biological
and technical variation. The 2015 study of the Proteome Informatics
Research Group (iPRG) of the Association of Biomolecular Resource
Facilities (ABRF) aimed to evaluate the effects of the statistical
analysis on the accuracy of the results. The study used LCâtandem
mass spectra acquired from a controlled mixture, and made the data
available to anonymous volunteer participants. The participants used
methods of their choice to detect differentially abundant proteins,
estimate the associated fold changes, and characterize the uncertainty
of the results. The study found that multiple strategies (including
the use of spectral counts versus peak intensities, and various software
tools) could lead to accurate results, and that the performance was
primarily determined by the analystsâ expertise. This manuscript
summarizes the outcome of the study, and provides representative examples
of good computational and statistical practice. The data set generated
as part of this study is publicly available
ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LCâMS/MS Experiments
Detection of differentially abundant
proteins in label-free quantitative shotgun liquid chromatographyâtandem
mass spectrometry (LCâMS/MS) experiments requires a series
of computational steps that identify and quantify LCâMS features.
It also requires statistical analyses that distinguish systematic
changes in abundance between conditions from artifacts of biological
and technical variation. The 2015 study of the Proteome Informatics
Research Group (iPRG) of the Association of Biomolecular Resource
Facilities (ABRF) aimed to evaluate the effects of the statistical
analysis on the accuracy of the results. The study used LCâtandem
mass spectra acquired from a controlled mixture, and made the data
available to anonymous volunteer participants. The participants used
methods of their choice to detect differentially abundant proteins,
estimate the associated fold changes, and characterize the uncertainty
of the results. The study found that multiple strategies (including
the use of spectral counts versus peak intensities, and various software
tools) could lead to accurate results, and that the performance was
primarily determined by the analystsâ expertise. This manuscript
summarizes the outcome of the study, and provides representative examples
of good computational and statistical practice. The data set generated
as part of this study is publicly available
Maximum Composite Likelihood trees.
<p>(A) Putative Deformed wing virus (DWV) sequences. (B) Kashmir bee virus (KBV) sequences from <i>Vespula vulgaris and Apis mellifera</i> sampled (bold) and the best matching sequences on GenBank, together with their accession numbers and host species. The trees were based on 2000 bootstraps of a general time-reversible model with gamma distribution and invariant sites parameters (GTR + G(0.48) +I(200); lnL â550.04) for DWV and a general time-reversible model (GTR; lnL â195.50) for KBV in MEGA6. The estimates of levels of support shown below the nodes are bootstrap values greater than 50%. The trees are drawn to scale, with branch lengths measured in the number of substitutions per site.</p
The number of microbial taxa observed from the previously published literature and proteomics methods.
<p>(A) The number of microbial taxa observed in published studies examining <i>V</i>. <i>germanica</i> and <i>V</i>. <i>vulgaris</i>. The numbers at the top of the bars represent the number of published studies, e.g. there were nine published papers examining fungal in wasps from the invaded range. Inset is a graph showing the non-significant relationship (pâĽ0.218) between the number of taxa found and the number of studies for each microbial group. (B) Results from our proteomics survey of microbes associated with wasps from the native and invaded range. No viruses were observed in the proteomics analysis. The âotherâ category is from peptides indicating the presence of taxa including amoeba (<i>Acanthamoeba</i> sp.), a protozoan (<i>Babesia</i> sp.), and tapeworm (<i>Taenia</i> sp.).</p
Microbial communities in wasp samples from the four countries.
<p>(A) A Venn diagram showing the overlap and distinctiveness of microbial taxa from common wasps in the native (England and Belgium) and invaded range (New Zealand and Argentina). A total of 131 peptides from distinct microbial taxa were observed. Of these 131 microbial taxa, 39 taxa were shared between all countries, but different countries had between 9â14 distinct taxa. (B) Rarefaction curves showing the similarity of microbial taxa accumulation with increasing peptides sampled.</p
Maximum Composite Likelihood tree for putative 16S <i>Nosema</i> sequences from <i>Vespula vulgaris</i> sampled (bold) and the best matching sequences on GenBank, together with their accession numbers and sample collection locations (where available).
<p>The tree was based on 2000 bootstraps of a general time-reversible model with gamma distribution and invariant sites parameters (GTR + G(0.69) +I(0.0); lnL â928.456) in MEGA6. The estimates of levels of support shown below the nodes are bootstrap values greater than 50%. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.</p
Maximum Composite Likelihood tree for putative Actinobacteria sequences from <i>Vespula vulgaris</i> sampled (bold) and the best matching sequences on GenBank, together with their accession numbers and host species.
<p>The tree was based on 2000 bootstraps of a general time-reversible model with gamma distribution and invariant sites parameters; lnL â559.13) in MEGA6. The estimates of levels of support shown below the nodes are bootstrap values greater than 50%. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. Often GenBank sequences were equally well matched to the sequences from <i>V</i>. <i>vulgaris</i> and those displayed on the tree are not exhaustive (e.g. the Ireland sample matched equally well to multiple <i>Arthrobacter</i> sp.).</p
Sample locations for common wasps from the native (England and Belgium) and invaded range (New Zealand and Argentina).
<p>Twenty adult <i>V</i>. <i>vulgaris</i> worker wasps were collected from each of the four countries. In some cases, multiple wasps were collected in the same area, but never from the same nest. For Argentina, the restricted sampling area represents the latitudinal limits of their distribution at the time of sampling in 2013. Common wasps are distributed throughout New Zealand.</p